The field of the present disclosure relates generally to analyzing data, and more particularly, to a network-based method and system for analyzing weirdness of a plurality of variables, including ranking disparate data by how unexpected each datum is based on how each datum compares to calculated predictions over time.
Many entities in a variety of industries store large volumes of data. Such data may include, for example, financial transaction data. The data may be analyzed, or mined, to identify trends, anomalies, and/or patterns in the data. By identifying trends, anomalies, and/or patterns in the data, potential issues and/or problems can be identified and addressed.
At least some known data analysis systems utilize complex modeling algorithms to analyze data. These known systems require significant computational resources and/or experienced programmers to implement. In general, with respect to these known systems, the more complex the analysis process, the longer it takes to generate results.
At least some of these known data analysis systems only compare identical types of data, and do not compare different types of data against one another. For example, if one particular data parameter increases significantly, when viewed in isolation, it may be determined that the particular data parameter is an outlier and/or an anomaly. However, in actuality, other data parameters may have similarly increased as part of an overall pattern or trend, and thus, this believed outlier is not actually an outlier.
Accordingly, it would be desirable to provide a computer system configured to analyze complex data without requiring significant computational resources, and determine how unexpected data is by comparing the data to calculated predictions over time.